Time-Frequency Learning Machines
Résumé
Over the last decade, the theory of reproducing kernels has made a major breakthrough in the field of pattern recognition. It has led to new algorithms, with improved performance and lower computational cost, for non-linear analysis in high dimensional feature spaces. Our paper is a further contribution which extends the framework of the so-called kernel learning machines to time-frequency analysis, showing that some specific reproducing kernels allow these algorithms to operate in the time-frequency domain. This link offers new perspectives in the field of non-stationary signal analysis, which can benefit from the developments of pattern recognition and Statistical Learning Theory.
Domaines
Machine Learning [stat.ML] Traitement du signal et de l'image [eess.SP] Statistiques [math.ST] Traitement du signal et de l'image [eess.SP] Réseau de neurones [cs.NE] Apprentissage [cs.LG] Ordinateur et société [cs.CY] Vision par ordinateur et reconnaissance de formes [cs.CV] Intelligence artificielle [cs.AI]| Origine | Fichiers produits par l'(les) auteur(s) |
|---|
Loading...